1. Technical Field
The present disclosure relates to the technical field of an acoustic echo cancellation, in more particular, to a method for acoustic echo cancellation and a echo cancellation system thereof.
2. Description of Related Art
Acoustic echo interference usually occurs in a communication system and an audio system, such as mobile phone, video conference, telecommunication conference, VOIP phone and so on. There are two primary acoustic elements, which are woofer and microphone. When the two elements are disposed in an enclosure space or close to each other, where the soundwave energy is enough to transmit from the woofer to the microphone, its generating echo would greatly decrease the quality of communication and user may have an auditorily-uncomfortable feeling.
FIG. 1 illustrates a system block diagram depicting a system for acoustic echo cancellation in a conventional art. Referring to FIG. 1, the system includes a microphone 101, a speaker 102, a echo cancellation module 103 and a adaptive filter 104, wherein the adaptive filter 104 uses the adaptive filtering algorithm to generate a signal {circumflex over (d)}(n) approximated to the echo signal d(n) such that the echo cancellation module 103 could cancel the echo with the signal.
Adaptive filtering algorithms have been widely employed in many signal processing applications such as equalization, active noise control, acoustic echo cancellation, and biomedical engineering. The normalized least-mean-square (NLMS) adaptive filter is the most popular due to its simplicity. The stability of the basic NLMS is controlled by a fixed step-size μc. This parameter also governs the rate of convergence, speed of tracking ability and the amount of steady-state excess mean-square error (MSE). Aiming to solve the conflicting objectives of fast convergence and low excess MSE associated with the conventional NLMS, a number of variable step-size NLMS (VSS-NLMS) algorithms have been presented in the past two decades.
Kwong used the power of instantaneous error to derive a variable step-size LMS (VSS-LMS) filter. This VSS-LMS employs a larger step size when the estimation error is large, and vice versa. Aboulnasr pointed out that the advantageous performance of this VSS-LMS and several other variable step-size LMS algorithms is usually obtained in a high signal-to-noise environment. She then developed a scheme using the autocorrelation of errors to alleviate the influence of uncorrelated disturbance. Recently Shin, Sayed, and Song developed a variable step-size affine projection algorithm, which employs the norm of the filter coefficient error vector as a criterion for optimal variable step-size.
Another type of variable step-size algorithm is the regularized NLMS. Mandic derived a generalized normalized gradient descent (GNGD) algorithm, which updates the regularization parameter gradient adaptively. Choi presented a robust regularized NLMS (RR-NLMS) filter, which uses a normalized gradient to update the regularization parameter. It should be noted that the RR-NLMS is effectively a “sign GNGD” algorithm. While most variable step-size algorithms need to tune several parameters for better performance, Benesty introduced a relatively tuning-free nonparametric VSS-NLMS (NPVSS) algorithm.
This invention presents a new nonparametric algorithm, which employs the MSE and the estimated system noise power to control the step-size update. The motivation is that a large MSE increases step-size and a large system noise decreases step-size, and vice versa. Our new VSS-NLMS is easy to implement and gives very good performance.